Too many possibilities, too little direction
Generative AI, agents, automation, RAG, dashboards, copilots, local models, APIs. Everything seems interesting. Not everything deserves priority.
> diagnostic_sprint
Orbit Discovery Sprint
> core_services
Discover where to apply AI in your company before spending money on the wrong solution.
A strategic sprint to identify real AI, automation and corporate knowledge opportunities, prioritise them by impact and turn them into a clear action roadmap.
Most companies don't need to start by buying software. They need to understand where AI can generate value, which processes make sense, which data is ready, what risks exist and which use case should go first. Orbit Discovery Sprint helps you make that decision with business judgement, technical vision and execution focus.
2-3 week sprint for teams that want to apply AI with focus, privacy and operational return. No hype. No endless roadmap. No decorative innovation.
> service_signal
> private_ai > decision_layer > controlled_orbit
> problem
AI doesn't fail due to lack of potential. It fails due to starting wrong.
Many companies already know AI can help them. The problem is different: they don't know where to start. They start by trying random tools, requesting demos, copying what competitors do or letting each department experiment on their own. The result: pilots that don't scale, ownerless automations, poorly prepared data, confused teams and zero visible business impact.
Tools before strategy
Buying technology without understanding the process is an expensive way to accelerate chaos. AI doesn't fix poorly defined processes. Sometimes it makes them fail faster.
Scattered data and knowledge
Documents, folders, emails, spreadsheets, PDFs, meetings and knowledge in key people's heads. The company knows a lot, but can't always use it well.
Pilots without real impact
A demo can impress. A pilot must demonstrate value. Without a use case, metric and owner, you just have a pretty proof with an expiry date.
✗ The wrong question: What AI tool should we use? ✓ The right question: Which decision, process or knowledge area should we improve first?
> solution
We turn uncertainty into an action plan.
Orbit Discovery Sprint is the gateway to the LowOrbits ecosystem. During the sprint we analyse your context, processes, tools, internal knowledge, business priorities and maturity level to detect where AI can add value realistically. We don't look to "use AI everywhere". We look to find the first right moves. The result is a clear map of opportunities, risks, priorities and next steps.
> Detect real AI opportunities
> Prioritise use cases by impact and viability
> Identify technical, operational and privacy risks
> Avoid premature investments
> Align management, technology and teams
> Define a 30/60/90 roadmap
> Turn scattered ideas into actionable decisions
Before building, you need to decide what deserves to be built.
When an Orbit Discovery Sprint makes sense
This service works especially well when the company feels AI can help, but needs focus before investing.
We want to apply AI but don't know where to start
The management team sees opportunities, but isn't clear on which use case to prioritise.
> result Opportunity map and first action roadmap.
We have too many manual processes
The company detects inefficiencies and repetitive tasks, but doesn't know what to automate first.
> result Prioritisation of automatable processes by impact, risk and difficulty.
We have a lot of scattered internal knowledge
Manuals, documentation, processes and technical knowledge are fragmented.
> result Viability assessment for AI Knowledge Hub, corporate RAG or private knowledge system.
We want to train the team but don't know in what
The company wants to train in AI, but needs to connect training with real roles and cases.
> result Training proposal by profiles, departments and objectives.
We have an AI product or tool idea
There's a clear opportunity, but it's not yet defined as an MVP.
> result Hypothesis definition, initial scope and next product step.
We need to convince management or partners
There's interest, but clarity is missing to justify investment.
> result Executive document with opportunities, risks, priorities and initial business case.
> orbit_discovery_method
O.R.B.I.T. Discovery Method
A method to move from possibilities to priorities.
We don't start with models, tools or automations. We start by understanding how the business works, where value is lost and what decisions need better information, speed or consistency.
01
Observe
We analyse the current context: business, teams, processes, tools, documentation, data and operational friction.
Clear reading of the starting point.
02
Reveal
We detect AI, automation, corporate knowledge, training or internal tool development opportunities.
Initial opportunity map and relevant problems.
03
Balance
We evaluate each opportunity by impact, complexity, risk, privacy, data maturity and implementation capacity.
Prioritisation matrix.
04
Identify
We select the most sensible use cases to advance first and discard those that don't justify immediate investment.
Shortlist of recommended initiatives.
05
Trajectory
We turn priorities into a 30/60/90 roadmap with next steps, dependencies, resources and possible implementation routes.
Actionable roadmap and next move decision.
Strategy isn't about having many options. It's about knowing which one you shouldn't pursue yet.
Designed for companies that want to apply AI with direction, not improvisation
For CEOs
You want to understand where AI can generate real impact without depending on trends or vendors pushing their tool.
Strategic clarity and prioritised first moves.
For CTOs
You need to evaluate use cases, architecture, data and risks before each department starts integrating tools on their own.
More controlled technical decisions aligned with business.
For founders and startups
You want to use AI to operate better or build product, but need to prioritise fast without burning months on poorly focused experiments.
Focus, speed and validation.
For industrial companies
You have technical documentation, complex processes and concerns about privacy or industrial property.
Identify safe opportunities for private AI, automation or internal RAG.
For innovation teams
You need to turn ideas and internal pressure to "do something with AI" into concrete, evaluated and defensible initiatives.
Opportunity pipeline and executive roadmap.
What we deliver
Each sprint adapts to the company's context, but always ends with actionable clarity. We don't deliver a decorative report. We deliver a basis for deciding.
✓ AI Opportunity Map — Opportunity map where AI, automation or knowledge systems can add value. Includes opportunities by area, process, team or function.
✓ Prioritized Use Case Matrix — Prioritisation matrix by impact, difficulty, risk, privacy, data maturity and implementation effort.
✓ Risk & Readiness Assessment — Risk and internal readiness assessment: data, documentation, current tools, dependencies, privacy and adoption capacity.
✓ 30/60/90 Roadmap — 90-day action plan divided into clear phases: what to do first, what to prepare, what to validate, what not to touch yet.
✓ Recommended Next Move — Concrete recommendation for the next step: AI Knowledge Hub, AI Automation, AI App/MVP, AI Training, Private Audio, Intelligence Partner, or don't build yet.
✓ Executive Decision Brief — Executive document for management with conclusions, priorities, risks, estimated investment and recommended next decision.
Strategic discovery yes. Endless consulting, no.
Orbit Discovery Sprint is designed to generate clarity in a few weeks. It doesn't aim to solve the entire AI transformation. It aims to define the first right move.
No tool-first
We don't recommend tools before understanding process, context and objective.
Privacy by design
We evaluate privacy risks, sensitive data and intellectual property from the start.
Business-first evaluation
Each use case is evaluated by business impact, not by how technically interesting it sounds.
Scope control
The sprint doesn't try to solve everything. We prioritise decisions. Then execution happens in phases.
No AI theatre
If an initiative doesn't have a clear use case, metric or owner, we don't sell it as a priority.
Less spectacle. More direction.
> launch_diagnostic
Start with a controlled diagnostic.
Tell us where you are and what you want to achieve with AI. We'll tell you if it makes sense to start with Discovery.
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Frequently asked questions
An audit evaluates the current state. Orbit Discovery Sprint goes further: identifies opportunities, prioritises use cases, analyses risks and defines a roadmap. It doesn't stop at diagnosis. It ends in decision.
Normally between 2 and 3 weeks, depending on the number of areas, interviews, documentation and required depth.
Access to basic information about processes, tools, internal challenges and priorities. Everything doesn't need to be ordered. Part of the sprint involves understanding that chaos.
Not as the main objective. It defines what to implement first and how. Afterwards it can lead to AI Knowledge Hub, automation, training, MVP or Intelligence Partner.
Yes, if the company has processes, information or decisions that can improve with AI. You don't need to be a large corporation. You need a real problem and willingness to act.
Then the sprint will already have generated value. Sometimes the best decision is to prepare data, train the team or organise processes. AI isn't always the first step.
There are normally four routes: launch an AI Knowledge Hub pilot, automate a specific process, train a team, develop an MVP, or start an engagement with Intelligence Partner.
If there's already a specific process or need, we can move directly to a complementary service or pilot.
Before investing in AI, define your first orbit.
AI can improve processes, decisions, knowledge and productivity. But only if applied in the right place, with the right scope and with risks understood. Orbit Discovery Sprint helps you choose that first move.